


'''simple docstring'''


import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase_						:  int				  =						logging.get_logger(__name__)

UpperCAmelCase_						:  List[str]				  =						{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn.grep_linear': 'encoder.layers.*.attention.gru_rel_pos_linear',
    'self_attn.relative_attention_bias': 'encoder.layers.*.attention.rel_attn_embed',
    'self_attn.grep_a': 'encoder.layers.*.attention.gru_rel_pos_const',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase_						:  Any				  =						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				for attribute in key.split("""."""	):
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				if weight_type is not None:
								_SCREAMING_SNAKE_CASE					:			str							= getattr(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	).shape
				else:
								_SCREAMING_SNAKE_CASE					:			List[str]							= hf_pointer.shape

				assert hf_shape == value.shape, (
				    f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
				    f""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value
				elif weight_type == "weight_g":
								_SCREAMING_SNAKE_CASE					:			List[str]							= value
				elif weight_type == "weight_v":
								_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value
				elif weight_type == "bias":
								_SCREAMING_SNAKE_CASE					:			List[Any]							= value
				else:
								_SCREAMING_SNAKE_CASE					:			Dict							= value

				logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= []
				_SCREAMING_SNAKE_CASE					:			Any							= fairseq_model.state_dict()

				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= False
								if "conv_layers" in name:
												load_conv_layer(
												    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, hf_model.config.feat_extract_norm == """group"""				, )
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split("""w2v_model."""	)[-1] == name.split("""."""	)[0]:
																				_SCREAMING_SNAKE_CASE					:			List[str]							= True
																				if "*" in mapped_key:
																								_SCREAMING_SNAKE_CASE					:			List[Any]							= name.split(SCREAMING_SNAKE_CASE__	)[0].split("""."""	)[-2]
																								_SCREAMING_SNAKE_CASE					:			int							= mapped_key.replace("""*"""				, SCREAMING_SNAKE_CASE__	)
																				if "weight_g" in name:
																								_SCREAMING_SNAKE_CASE					:			Optional[int]							= """weight_g"""
																				elif "weight_v" in name:
																								_SCREAMING_SNAKE_CASE					:			Dict							= """weight_v"""
																				elif "bias" in name and "relative_attention_bias" not in name:
																								_SCREAMING_SNAKE_CASE					:			int							= """bias"""
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								_SCREAMING_SNAKE_CASE					:			List[str]							= """weight"""
																				else:
																								_SCREAMING_SNAKE_CASE					:			Optional[int]							= None

																				set_recursively(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
																continue
								if not is_used:
												unused_weights.append(SCREAMING_SNAKE_CASE__	)

				logger.warning(f"""Unused weights: {unused_weights}"""	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= full_name.split("""conv_layers."""	)[-1]
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= name.split("""."""	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= int(items[0]	)
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= int(items[1]	)

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												_SCREAMING_SNAKE_CASE					:			List[str]							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												_SCREAMING_SNAKE_CASE					:			List[Any]							= value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""	)
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												_SCREAMING_SNAKE_CASE					:			List[str]							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												_SCREAMING_SNAKE_CASE					:			int							= value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
				else:
								unused_weights.append(SCREAMING_SNAKE_CASE__	)







@torch.no_grad()
def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=None	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[Any]							= torch.load(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			List[str]							= WavLMConfigOrig(checkpoint["""cfg"""]	)
				_SCREAMING_SNAKE_CASE					:			int							= WavLMOrig(SCREAMING_SNAKE_CASE__	)
				model.load_state_dict(checkpoint["""model"""]	)
				model.eval()

				if config_path is not None:
								_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE__	)
				else:
								_SCREAMING_SNAKE_CASE					:			Any							= WavLMConfig()

				_SCREAMING_SNAKE_CASE					:			Tuple							= WavLMModel(SCREAMING_SNAKE_CASE__	)

				recursively_load_weights(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  List[str]				  =						argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				UpperCAmelCase_						:  Dict				  =						parser.parse_args()
				convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)








'''simple docstring'''


import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCAmelCase_						:  List[Any]				  =						'.'


if __name__ == "__main__":
				UpperCAmelCase_						:  Any				  =						os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
				UpperCAmelCase_						:  Dict				  =						[]
				UpperCAmelCase_						:  Union[str, Any]				  =						[]
				with open(doctest_file_path) as fp:
								for line in fp:
												UpperCAmelCase_						:  int				  =						line.strip()
												UpperCAmelCase_						:  str				  =						os.path.join(REPO_PATH, line)
												if not (os.path.isfile(path) or os.path.isdir(path)):
																non_existent_paths.append(line)
												all_paths.append(path)
				if len(non_existent_paths) > 0:
								UpperCAmelCase_						:  int				  =						'\n'.join(non_existent_paths)
								raise ValueError(F"`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}")
				if all_paths != sorted(all_paths):
								raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')




